Generative modeling attempts to learn variable interdependencies such that the model is able to generate realistic samples that abide by certain characteristics. Variational Autoencoders [1]}, Generative adversarial networks [2]} and normalizing flows [3]} are examples for approaches that try to estimate the underlying data distribution from which they then sample to produce new data. Causal inference in generative modeling is a relatively underdeveloped field, especially in the context of medical imaging due to the inherit difficulty to acquire good quality training signals for the counterfactual samples.
